Flood monitoring: A hydrologically guided method for infilling incomplete flood inundation maps derived from satellite images

• FEMM is a new non-iterative EOF-based infilling method. • FEMM requires a library of spatial features from either observations or model simulations. • FEMM shows good ability to complete Landsat-based inundation maps. • An initial DEM-based local infilling further improves FEMM performance. • FEMM is applicable to any satellite-based inundation maps. Floods are a leading cause of disaster-related fatalities and economic losses. There is a growing use of remotely sensed information on flood extents to evaluate flooding conditions and to assist in future event predictions. However, remotely sensed products often lack the necessary spatial or temporal resolution needed for disaster mitigation. Recent research has attempted to solve this issue by utilizing high-spatial-resolution optical satellite images collected at sparse time intervals (e.g., Landsat 8/9) to enhance coarse-spatial-resolution images (e.g., Moderate Resolution Imaging Spectroradiometer or MODIS), taking advantage of the latter’s high temporal resolution. Nevertheless, the usability of optical images is limited by clouds and aerosols, resulting in missing or poor-quality pixels. This study addresses this issue by developing a feature extraction, mixing and matching (FEMM) methodology to infill missing inundation information in optical satellite derived flood inundation maps. In the FEMM approach, dominant spatial features of flood extents are extracted from long-term simulation results from hydrological and simple flood inundation models. Missing pixel values for a satellite image are then inferred from the non-missing parts of the image based on the dominant spatial features. The Empirical Orthogonal Functions (EOF) technique is used in both the feature extraction and flood extent construction. To evaluate the efficacy of the FEMM methodology, we use synthetic cloud masks of varying patterns and coverage sampled from real images with clouds. The synthetic cloud masks are applied to a cloud free image to produce degraded images, which are then infilled and compared with the original image. A combination of an initial terrain-based local infilling and the FEMM is shown to be highly effective in most missing data scenarios, achieving critical success indices >80%. An exception is when an extensively large patch (for example half of the image) is completely missing from the image at either the upstream or downstream end of the floodplain. The approach has the capacity to enhance remote sensing images by infilling incomplete inundation maps based on hydrological spatial patterns and topographic information, thereby improving flood monitoring for emergency services.